可持续TOD建设视角下的轨道站点客流潜力模型构建

doi:10.12082/dqxxkx.2022.220315

摘要/Abstract

摘要：

轨道是缓解城市交通问题的重要设施，增加出行者选择轨道出行的概率，有利于交通与土地利用协调和TOD可持续发展。本文提出站点客流潜力的概念，并根据复杂网络特征和出行模式构建站点客流潜力模型，通过协调潜力值与实际客流为TOD研究提供新视角。以北京市轨道站点POI数据构建Space-L模型，并依据站点客流潜力模型计算北京市364个站点的客流潜力。研究发现：① 本文提出的站点客流潜力具有吸引力和承载力两类内涵，能定量分析站点空间与站域客流的协调情况；② 北京市站点客流潜力值空间分布为“核心-边缘”模式，区间概率分布为等差数列分类的指数分布和等比数列分类的正态分布；③ 根据出行目的设置四类出行情景。不同情景下出行者选择轨道出行的概率具有差异，早高峰和晚高峰情景下轨道出行概率大，受潜力值影响小，非工作出行情景下轨道出行概率小，受潜力值影响大。实例分析表明相比于单独考虑复杂网络特征，潜力值具有更好的可解释性和科学性；④ 耦合度C $<$ 0.5时认定站点失调，其中标准化的实际客流与潜力值的比值Z $>$ 1，表示客流过饱和，如西二旗等，会造成站点拥堵，Z $<$ 1表示站点交通地理优势未充分发挥，如北运河西等。北京市轨道交通需协调优化以提升效率，实现TOD可持续发展。

关键词: TOD, 复杂网络, 站点客流潜力模型, 北京市, 出行行为模式, 轨道交通, 客流潜力, 低碳交通

Abstract:

Rail transit is an important facility to alleviate urban traffic problems, and increasing the probability of passengers choosing rail transit to travel is conducive to the coordination of transportation and land use and the sustainable development of TOD. This paper proposes the concept of rail transit station passenger flow potential and constructs a station passenger flow potential calculation model based on the connotation of complex network eigenvalues and travel patterns, which provides a new perspective for TOD research by coordinating potential values with actual passenger flows. The Space-L model is constructed using POI data of Beijing railway stations, and the passenger flow potential of 364 stations in Beijing is calculated based on the station passenger flow potential model. The results show that 1) The station passenger flow potential values proposed in this paper have the dual connotation of attractiveness and carrying capacity, and can be used to quantitatively analyse the coordination between the Station Space and Station Area Traffic; 2) The spatial distribution of station passenger flow potential values in Beijing shows a "core-edge" pattern, with the most compact circles in non-work travel scenarios. The probability of the interval shows an exponential distribution for the isometric classification and a normal distribution for the isometric classification; 3) Four travel scenarios are set up according to the purpose of travel. The probability of travellers choosing rail travel differs in different scenarios. The probability of choosing rail travel is higher in the morning peak and evening peak scenarios and is less influenced by the potential value, while the probability of choosing rail travel is lower in the non-work travel scenario and is more influenced by the potential value, which is in line with reality. The verification example shows that the potential value has better interpretability and scientific validity than simply considering complex network features; 4) The coupling degree of C< 0.5 identifies the station as out of tune, where the ratio of actual passenger flow to potential value Z $>$ 1, indicating that the station is over-saturated and are prone to congestion problems, such as Xi'erqi Station, Tiantongyuanbei Station, Tiantongyuan Station, Fengtai Kejiyuan Station, etc. If Z $<$ 1, it means that the geographical advantages of the station's traffic have not been fully exploited and the passenger flow can be further improved, such as Beijingnan Station, Beiyunhexi Station, Wangjingdong Station, etc. Our results indicate that Beijing's rail transport needs to be optimised in order to improve its efficiency and achieve sustainable TOD construction.

Key words: TOD, Complex networks, Site traffic potential model, Beijing Municipality, travel behaviour patterns, rail transport, passenger flow potential values, low carbon transport

谭德明, 李延欢. 可持续TOD建设视角下的轨道站点客流潜力模型构建[J]. 地球信息科学学报, 2022, 24(12): 2356-2372.DOI:10.12082/dqxxkx.2022.220315

TAN Deming, LI Yanhuan. Modelling the Passenger Flow Potential of Rail Stations from the Perspective of Sustainable TOD Construction[J]. Journal of Geo-information Science, 2022, 24(12): 2356-2372.DOI:10.12082/dqxxkx.2022.220315

图/表 14

图1

表1

城市轨道交通出行情景设定

情景编号	情景名称	出行目的	情景描述	中心性赋权	情景定义
情景1	均衡情景	工作、游憩、购物、居住	长时间来看，站点3类特征具有同等重要的作用	$α = 1 / 3 、 β = 1 / 3 、 γ = 1 / 3$	综合区位优势
情景2	早高峰情景（7:00—9:00）	工作	对站点的快速疏解和快速到达目的地的特征要求较大，对客流交汇要求小	$α = 0.4 、 β = 0.2 、 γ = 0.4$	居住区位优势
情景3	非工作出行情景（周末、休息）	游憩、购物	对客流交汇较为重视，对快速疏解和快速到达目的地特征要求不高	$α = 0.3 、 β = 0.4 、 γ = 0.3$	经济区位优势
情景4	晚高峰情景（18:00—21:00）	居住、游憩、购物	注重一定的快速疏解功能，对客流交汇和快速到达目的地的需求较弱	$α = 0.4 、 β = 0.3 、 γ = 0.3$	工作区位优势

表1

图2

图3

图4

图5

表2

图6

图7

图8

图9

图10

表3

4类情景与实际客流回归分析结果

回归分析	各项参数	线路客流潜力	线路复杂网络	早高峰进站	早高峰出站	早高峰换乘
情景1（均衡情景）	R²	0.7204	0.6768	0.0018	0.0820	0.0226
	B	2.6659	1.1120	-0.1459	1.8185	0.4311
	C	0.9175	0.9453	0.7121	0.7943	0.7171
情景2（早高峰情景）	R²	0.7244	0.6739	0.0004	0.0616	0.0150
	B	2.5304	$1$ .0743	-0.0603	1.3168	0.2905
	C	0.9189	0.9424	0.7159	0.7884	0.7000
情景3（非工作出行情景）	R²	0.7169	0.6757	0.0028	0.0899	0.0257
	B	2.7335	1.1309	-0.1888	2.0694	0.5014
	C	0.9169	0.9466	0.7095	0.7943	0.7222
情景4（晚高峰情景）	R²	0.7202	0.6570	0.0013	0.0783	0.0207
	B	2.6315	1.8535	-0.1220	1.7325	0.3994
	C	0.9188	0.9466	0.7139	0.7930	0.7075

表3

表4

耦合度检验失调的站点

耦合度检验失调	早高峰进站	早高峰出站	早高峰换乘
情景1（均衡情景）	天通苑北（0.245，Z $>$ 1），古城（0，Z $>$ 1），北京南站（0.363，Z $<$ 1），北运河西（0，Z $<$ 1）	丰台科技园（0，Z $>$ 1），望京东（0，Z $<$ 1）	西二旗（0.318，Z $>$ 1），西苑（0，Z $>$ 1），国贸（0，Z $<$ 1）
情景2（早高峰情景）	天通苑北（0.281，Z $>$ 1）），古城（0，Z $>$ 1）），北京南站（0.363，Z $<$ 1），北运河西（0，Z $<$ 1）	丰台科技园（0，Z $>$ 1），望京东（0，Z $<$ 1）	郭公庄（0.431，Z $>$ 1），西二旗（0.236，Z $>$ 1），西苑（0，Z $>$ 1），国贸（0，Z $<$ 1）
情景3（非工作出行情景）	天通苑北（0.227，Z $>$ 1），天通苑（0.488，Z $>$ 1），古城（0，Z $>$ 1），北京南站（0.363，Z $<$ 1），北运河西（0，Z $<$ 1）	丰台科技园（0，Z $>$ 1），望京东（0，Z $<$ 1）	西二旗（0.343，Z $>$ 1），西苑（0，Z $>$ 1），国贸（0，Z $<$ 1）
情景4（晚高峰情景）	天通苑北（0.244，Z $>$ 1），古城（0，Z $>$ 1），北京南站（0.363，Z $<$ 1），北运河西（0，Z $<$ 1）	丰台科技园（0，Z $>$ 1），望京东（0，Z $<$ 1）	郭公庄（0.486，Z $>$ 1），西二旗（0.242，Z $>$ 1），西苑（0，Z $>$ 1），国贸（0，Z $<$ 1）

表4

参考文献 43

[1]	潘海啸. 面向低碳的城市空间结构——城市交通与土地使用的新模式[J]. 城市发展研究, 2010, 17(1):40-45.
	[ Pan H X. Urban spatial structure toward low carbon: New urban transport and land use model[J]. Urban Studies, 2010, 17(1):40-45. ] DOI:10.3969/j.issn.1006-3862.2010.01.007 doi: 10.3969/j.issn.1006-3862.2010.01.007
[2]	陈飞, 诸大建, 许琨. 城市低碳交通发展模型、现状问题及目标策略——以上海市实证分析为例[J]. 城市规划学刊, 2009(6):39-46.
	[ Chen F, Zhu D J, Xu K. Research on urban low-carbon traffic model, current situation and strategy: An empirical analysis of Shanghai[J]. Urban Planning Forum, 2009(6):39-46. ] DOI:10.3969/j.issn.1000-3363.2009.06.008 doi: 10.3969/j.issn.1000-3363.2009.06.008
[3]	杨向前. 民生视域下我国特大型城市交通拥堵问题研究[J]. 城市规划, 2012, 36(1):92-96.
	[ Yang X Q. A study on traffic congestion of metropolis China in view of People’s livelihood[J]. City Planning Review, 2012, 36(1):92-96. ]
[4]	汪光焘. 中国城市交通问题、对策与理论需求[J]. 城市交通, 2016, 14(6):1-9.
	[ Wang G T. Urban transportation in China: Problems, policies and integrating theory with practice[J]. Urban Transport of China, 2016, 14(6):1-9. ] DOI:10.13813/j.cn11-5141/u.2016.0601 doi: 10.13813/j.cn11-5141/u.2016.0601
[5]	冯志新, 陈颖彪, 千庆兰, 等. 东莞市交通路网格局对城市空间扩张影响研究[J]. 地球信息科学学报, 2014, 16(1):79-86. doi: 10.3724/SP.J.1047.2014.00079
	[ Feng Z X, Chen Y B, Qian Q L, et al. Relationship between the structure of urban traffic network and urban spatial expansion: A case study of Dongguan City[J]. Journal of Geo-Information Science, 2014, 16(1):79-86. ] DOI:10.3724/SP.J.1047.2014.00079 doi: 10.3724/SP.J.1047.2014.00079
[6]	戢晓峰, 姜莉, 陈方. 欠发达地区城市公交底线公平的空间分异特征及成因分析——以云南省为例[J]. 人文地理, 2018, 33(1):124-129.
	[ Ji X F, Jiang L, Chen F. Baseline equality of spatial differentiation characteristics and cause analysis of urban public transport in underdeveloped areas: A case of Yunnan Province[J]. Human Geography, 2018, 33(1):124-129. ] DOI:10.13959/j.issn.1003-2398.2018.01.016. doi: 10.13959/j.issn.1003-2398.2018.01.016
[7]	张明, 刘菁. 适合中国城市特征的TOD规划设计原则[J]. 城市规划学刊, 2007(1):91-96.
	[ Zhang M, Liu J. The Chinese edition of transit-oriented development[J]. Urban Planning Forum, 2007(1):91-96. ] DOI:10.3969/j.issn.1000-3363.2007.01.018 doi: 10.3969/j.issn.1000-3363.2007.01.018
[8]	裴莹莹, 杨敏明. 城市轨道交通与常规公交耦合关系分析[J]. 交通与运输, 2021, 34(S1):121-125,141.
	[ Pei Y Y, Yang M M. Coupling relation analysis between rail transit and bus[J]. Traffic & Transportation, 2021, 34(S1):121-125,141. ] DOI:10.3969/j.issn.1671-3400.2021.z1.027 doi: 10.3969/j.issn.1671-3400.2021.z1.027
[9]	高玉祥, 韩峰, 段晓峰. “一带一路”节点城市群轨道交通空间布局分析——以兰州——西宁城市群为例[J]. 测绘通报, 2018(12):114-118.
	[ Gao Y X, Han F, Duan X F. Study on the space layout of rail transit development of “the Belt and Road” node urban agglomeration: A case study of Lanzhou-Xining City region[J]. Bulletin of Surveying and Mapping, 2018(12):114-118. ] DOI:10.13474/j.cnki.11-2246.2018.0395 doi: 10.13474/j.cnki.11-2246.2018.0395
[10]	胡昂, 刘杰, 李想, 等. 多中心城市轨道交通典型站域的土地利用特征演化研究——以日本东京为例[J]. 西安建筑科技大学学报(自然科学版), 2021, 53(5):746-757.
	[ Hu A, Liu J, Li X, et al. Study on the evolution of land use characteristics in typical station areas of multi-center urban rail transit: Taking Tokyo as an example[J]. Journal of Xi'an University of Architecture & Technology (Natural Science Edition), 2021, 53(5):746-757. ] DOI:10.15986/j.1006-7930.2021.05.018 doi: 10.15986/j.1006-7930.2021.05.018
[11]	任鹏, 彭建东, 杨红, 等. 武汉市轨道交通站点周边地区职住平衡与建成环境的关系研究[J]. 地球信息科学学报, 2021, 23(7):1231-1245. doi: 10.12082/dqxxkx.2021.200662
	[ Ren P, Peng J D, Yang H, et al. Relationship between jobs-housing balance and built environment in areas around urban rail transit stations of Wuhan[J]. Journal of Geo-information Science, 2021, 23(7):1231-1245. ] DOI:10.12082/dqxxkx.2021.200662 doi: 10.12082/dqxxkx.2021.200662
[12]	曹哲静. 城市商业中心与交通中心的叠合与分异:基于复杂网络分析的东京轨道交通网络与城市形态耦合研究[J]. 国际城市规划, 2020, 35(3):42-53.
	[ Cao Z J. Configuration of urban commercial centers and transport centers: Evidence from Tokyo transit network and urban morphology based on the complex network analysis[J]. Urban Planning International, 2020, 35(3):42-53. ] DOI:10.19830/j.upi.2018.157 doi: 10.19830/j.upi.2018.157
[13]	滕丽, 钟楚捷, 蔡砥. 广州市地铁TOD站域的空间类型分异——基于“节点—场所—联系”耦合度模型的研究[J]. 经济地理, 2022, 42(4):103-111.
	[ Teng L, Zhong C J, Cai D. Study on the spatial type differentiation of Guangzhou metro TOD zones: Based on “node-place-linkage” coupling model[J]. Economic Geography, 2022, 42(4):103-111. ] DOI:10.15957/j.cnki.jjdl.2022.04.012 doi: 10.15957/j.cnki.jjdl.2022.04.012
[14]	丁川, 王耀武, 林姚宇. 公交都市战略与TOD模式关系探析——基于低碳出行的视角[J]. 城市规划, 2013, 37(11):54-61.
	[ Ding C, Wang Y W, Lin Y Y. Exploration on the relationship between transit-city strategy and TOD mode: From the perspective of low carbon travel[J]. City Planning Review, 2013, 37(11):54-61. ]
[15]	Cervero R, Sarmiento O L, Jacoby E, et al. Influences of built environments on walking and cycling: Lessons from Bogotá[J]. International Journal of Sustainable Transportation, 2009, 3(4):203-226. DOI:10.1080/15568310802178314 doi: 10.1080/15568310802178314
[16]	宋文杰, 史煜瑾, 朱青, 等. 基于节点—场所模型的高铁站点地区规划评价——以长三角地区为例[J]. 经济地理, 2016, 36(10):18-25,38.
	[ Song W J, Shi Y J, Zhu Q, et al. Evaluation on planning of high-speed rail station area based on node-place model in Yangtze River Delta area[J]. Economic Geography, 2016, 36(10):18-25,38. ] DOI:10.15957/j.cnki.jjdl.2016.10.003 doi: 10.15957/j.cnki.jjdl.2016.10.003
[17]	刘雨菡, 鲍梓婷, 田文豪. TOD站城融合发展路径与广州实践:多层级空间治理与协作式规划设计[J]. 规划师, 2022, 38(2):5-15.
	[ Liu Y H, Bao Z T, Tian W H. Station-city integrate development path and practice in Guangzhou TOD: Multi-level spatial governance and collaborative planning[J]. Planners, 2022, 38(2):5-15. ] DOI:10.3969/j.issn.1006-0022.2022.02.001 doi: 10.3969/j.issn.1006-0022.2022.02.001
[18]	王智鹏, 罗霞. 综合交通系统条件下城市轨道交通线网规模测算[J]. 长安大学学报(自然科学版), 2015, 35(S1):193-197.
	[ Wang Z P, Luo X. Scale calculating on urban rail transit network under the condition of comprehensive transportation system[J]. Journal of Chang'an University (Natural Science Edition), 2015, 35(S1):193-197. ] DOI:10.19721/j.cnki.1671-8879.2015.s1.040 doi: 10.19721/j.cnki.1671-8879.2015.s1.040
[19]	雷斌, 张源, 郝亚睿, 等. 城市轨道交通短期客流预测研究进展[J]. 长安大学学报(自然科学版), 2022, 42(1):79-96.
	[ Lei B, Zhang Y, Hao Y R, et al. Research progress on short-term passenger flow forecast model of urban rail transit[J]. Journal of Chang'an University (Natural Science Edition), 2022, 42(1):79-96. ] DOI:10.19721/j.cnki.1671-8879.2022.01.005 doi: 10.19721/j.cnki.1671-8879.2022.01.005
[20]	方礼君. 城市轨道交通客流相关问题研究[D]. 上海: 同济大学, 2008.
	[ Fang L J. The correlative issue study of urban mass transit passenger liner[D]. Shanghai: Tongji University, 2008. ]
[21]	Brunsdon C, Fotheringham A S, Charlton M E. Geographically weighted regression: A method for exploring spatial nonstationarity[J]. Geographical Analysis, 1996, 28(4):281-298. DOI:10.1111/j.1538-4632.1996.tb00936.x doi: 10.1111/j.1538-4632.1996.tb00936.x
[22]	Wang C H, Chen N. A geographically weighted regression approach to investigating the spatially varied built-environment effects on community opportunity[J]. Journal of Transport Geography, 2017, 62:136-147. DOI:10.1016/j.jtrangeo.2017.05.011 doi: 10.1016/j.jtrangeo.2017.05.011
[23]	Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684):440-442. DOI:10.1038/30918 doi: 10.1038/30918
[24]	Barabási, Albert. Emergence of scaling in random networks[J]. Science, 1999, 286(5439),509-512. DOI:10.126/science.286.5439.509 doi: 10.1126/science.286.5439.509 pmid: 10521342
[25]	Louf R, Roth C, Barthelemy M. Scaling in transportation networks[J]. PLoS One, 2014, 9(7):e102007. DOI:10.371/journal.pone.0102007 doi: 10.371/journal.pone.0102007
[26]	陈娱, 许珺. 考虑地理距离的复杂网络社区挖掘算法[J]. 地球信息科学学报, 2013, 15(3):338-344. doi: 10.3724/SP.J.1047.2013.00338
	[ Chen Y, Xu J. A distance-based method of community detection in complex networks[J]. Journal of Geo-Information Science, 2013, 15(3):338-344. ] DOI:10.3724/SP.J.1047.2013.00338 doi: 10.3724/SP.J.1047.2013.00338
[27]	Ni M, He Q, Gao J. Forecasting the subway passenger flow under event occurrences with social media[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(6):1623-1632. DOI:10.1109/TITS.2016.2611644 doi: 10.1109/TITS.2016.2611644
[28]	Wei Y, Lin S, Chu R, et al. A method of grading subway stations[J]. Procedia Engineering, 2016, 137:806-810. DOI:10.1016/j.proeng.2016.01.319. doi: 10.1016/j.proeng.2016.01.319
[29]	Xu Q, Mao B H, Bai Y. Network structure of subway passenger flows[J]. Journal of Statistical Mechanics: Theory and Experiment, 2016, 2016(3):033404. DOI:10.1088/1742-5468/2016/03/033404 doi: 10.1088/1742-5468/2016/03/033404
[30]	Yang Y H, Liu Y X, Zhou M X, et al. Robustness assessment of urban rail transit based on complex network theory: A case study of the Beijing Subway[J]. Safety Science, 2015, 79:149-162. DOI:10.1016/j.ssci.2015.06.006 doi: 10.1016/j.ssci.2015.06.006
[31]	Xia F, Wang J Z, Kong X J, et al. Ranking Station importance with human mobility patterns using subway network datasets[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(7):2840-2852. DOI:10.1109/TITS.2019.2920962 doi: 10.1109/TITS.2019.2920962
[32]	Sun S W, Li H Y, Xu X Y. A key station identification method for urban rail transit: A case study of Beijing subway[J]. PROMET - Traffic&Transportation, 2017, 29(3):267-273. DOI:10.7307/ptt.v29i3.2133 doi: 10.7307/ptt.v29i3.2133
[33]	Wang L, An M, Jia L M, et al. Application of complex network principles to key station identification in railway network efficiency analysis[J]. Journal of Advanced Transportation, 2019, 2019:1574136. DOI:10.1155/2019/1574136 doi: 10.1155/2019/1574136
[34]	Wang Z J, Jia L M, Ma X P, et al. Accessibility-oriented performance evaluation of high-speed railways using a three-layer network model[J]. Reliability Engineering & System Safety, 2022, 222:108411. DOI:10.1016/j.ress.2022.108411 doi: 10.1016/j.ress.2022.108411
[35]	Kong X J, Xu Z Z, Shen G J, et al. Urban traffic congestion estimation and prediction based on floating car trajectory data[J]. Future Generation Computer Systems, 2016, 61:97-107. DOI:10.1016/j.future.2015.11.013 doi: 10.1016/j.future.2015.11.013
[36]	王志如, 张满银. 北京地铁网络时空演化特征评估及演化机制[J]. 经济地理, 2021, 41(4):48-56.
	[ Wang Z R, Zhang M Y. Spatio-temporal evolution characteristics of Beijing subway network and its evolution mechanism[J]. Economic Geography, 2021, 41(4):48-56. ] DOI:10.15957/j.cnki.jjdl.2021.04.007 doi: 10.15957/j.cnki.jjdl.2021.04.007
[37]	Derrible S, Kennedy C. The complexity and robustness of metro networks[J]. Physica A: Statistical Mechanics and Its Applications, 2010, 389(17):3678-3691. DOI:10.1016/j.physa.2010.04.008 doi: 10.1016/j.physa.2010.04.008
[38]	Liu Y, Kang C G, Gao S, et al. Understanding intra-urban trip patterns from taxi trajectory data[J]. Journal of Geographical Systems, 2012, 14(4):463-483. DOI:10.1007/s10109-012-0166-z doi: 10.1007/s10109-012-0166-z
[39]	韩昊英, 于翔, 龙瀛. 基于北京公交刷卡数据和兴趣点的功能区识别[J]. 城市规划, 2016, 40(6):52-60.
	[ Han H Y, Yu X, Long Y. Identifying urban functional zones using bus smart card data and points of interest in Beijing[J]. City Planning Review, 2016, 40(6):52-60. ] DOI:10.11819/cpr20160609a doi: 10.11819/cpr20160609a
[40]	Ajzen I. The theory of planned behavior[J]. Organizational Behavior and Human Decision Processes, 1991, 50(2):179-211. DOI:10.1016/0749-5978(91)90020-T doi: 10.1016/0749-5978(91)90020-T
[41]	Ajzen I. The theory of planned behaviour: Reactions and reflections[J]. Psychology & Health, 2011, 26(9):1113-1127. DOI:10.1080/08870446.2011.613995 doi: 10.1080/08870446.2011.613995
[42]	Schläpfer M, Dong L, O’Keeffe K, et al. The universal visitation law of human mobility[J]. Nature, 2021, 593(7860):522-527. DOI:10.1038/s41586-021-03480-9 doi: 10.1038/s41586-021-03480-9
[43]	Wegener M, Fuerst F. Land-use transport interaction: State of the art[R]. Manila: Urban Regional, 2004.